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Record W4387306506 · doi:10.2175/193864718825158991

Case Study of the Lone Star Dairy Products Wastewater Treatment Plant Expansion Project

2023· article· en· W4387306506 on OpenAlexaboutno aff
Daniel Bertoldo, Mike Allison, Shannon Grant

Bibliographic record

VenueProceedings of the Water Environment Federation · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
Fundersnot available
KeywordsWastewaterSewage treatmentReuseAnaerobic digestionEnvironmental scienceWaste managementEnvironmental engineeringEngineeringBiologyEcology

Abstract

fetched live from OpenAlex

Case Study of the Lone Star Dairy Products Wastewater Treatment Plant Expansion ProjectAbstractThis paper will provide a case study of the Lone Star Dairy Products wastewater pretreatment plant, including a description of ADI-BVF® technology used to anaerobically digest the raw wastewater, process benefits of low-rate anaerobic digestion of dairy wastewater, upgrades made to the previous pretreatment system, and a review of the operating results in the two years since the pretreatment plant expansion.This paper will provide a case study of the Lone Star Dairy Products wastewater pretreatment plant, including a description of ADI-BVF technology used to anaerobically digest the raw wastewater, process benefits of low-rate anaerobic digestion of dairy wastewater, upgrades made to the previous pretreatment system, and a review of the operating results in the two years since the pretreatment plant expansion.SpeakerBertoldo, DanielPresentation time14:00:0014:30:00Session time13:30:0015:00:00SessionFood &amp; Beverage: Upgrades and TroubleshootingSession locationRoom S403a - Level 4TopicFacility Operations and Maintenance, Intermediate Level, Sustainability and Climate Change, Water Reuse and ReclamationTopicFacility Operations and Maintenance, Intermediate Level, Sustainability and Climate Change, Water Reuse and ReclamationAuthor(s)Bertoldo, DanielAuthor(s)D. Bertoldo <sup>1</sup>; M. Allison <sup>2 </sup>; M. Allison <sup>2</sup>; S. Zelaya <sup>1</sup>; D. Bertoldo <sup>1</sup>; S. Grant <sup>2</sup>;Author affiliation(s)Evoqua Water Technologies Canada Ltd., 370 Wilsey Road, Fredericton, NB, Canada E3B 6E9 <sup>1</sup>; Evoqua Water Technologies Canada Ltd., 370 Wilsey Road, Fredericton, NB, Canada E3B 6E9 <sup>2 </sup>; Evoqua Water Technologies <sup>2</sup>; Evoqua Water Technologies <sup>1</sup>; Evoqua Water Technologies <sup>1</sup>; Evoqua Water Technologies <sup>2</sup>;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825158991Volume / Issue Content sourceWEFTECCopyright2023Word count14

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.214
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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